# PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound

arXiv: https://arxiv.org/abs/2408.05372 · HTML: https://mlsanity.com/paper/2408.05372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023)
- [Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models](https://mlsanity.com/paper/2310.19721/) (2023)
- [PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts](https://mlsanity.com/paper/2404.15028/) (2024)
- [False Negative/Positive Control for SAM on Noisy Medical Images](https://mlsanity.com/paper/2308.10382/) (2023)
- [Cats: Complementary CNN and Transformer Encoders for Segmentation](https://mlsanity.com/paper/2208.11572/) (2022)
- [Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View](https://mlsanity.com/paper/2206.14746/) (2022)
- [Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts](https://mlsanity.com/paper/2311.07806/) (2023)
- [Novel OCT mosaicking pipeline with Feature- and Pixel-based registration](https://mlsanity.com/paper/2311.13052/) (2023)
- [Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2404.14837/) (2024)
- [Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images](https://mlsanity.com/paper/2407.08020/) (2024)

## Abstract (arXiv)

Placenta volume measured from 3D ultrasound (3DUS) images is an important tool for tracking the growth trajectory and is associated with pregnancy outcomes. Manual segmentation is the gold standard, but it is time-consuming and subjective. Although fully automated deep learning algorithms perform well, they do not always yield high-quality results for each case. Interactive segmentation models could address this issue. However, there is limited work on interactive segmentation models for the placenta. Despite their segmentation accuracy, these methods may not be feasible for clinical use as they require relatively large computational power which may be especially prohibitive in low-resource environments, or on mobile devices. In this paper, we propose a lightweight interactive segmentation model aiming for clinical use to interactively segment the placenta from 3DUS images in real-time. The proposed model adopts the segmentation from our fully automated model for initialization and is designed in a human-in-the-loop manner to achieve iterative improvements. The Dice score and normalized surface Dice are used as evaluation metrics. The results show that our model can achieve superior performance in segmentation compared to state-of-the-art models while using significantly fewer parameters. Additionally, the proposed model is much faster for inference and robust to poor initial masks. The code is available at https://github.com/MedICL-VU/PRISM-placenta.